MC-MinH: Metagenome Clustering using Minwise based Hashing

Current bio-technologies allow sequencing of genomes from multiple organisms, that co-exist as communities within ecological environments. This collective genomic process (called metagenomics) has spurred the development of several computational tools for the quantification of abundance, diversity and role of different species within different communities. Unsupervised clustering algorithms (also called binning algorithms) have been developed to group similar metagenome sequences. We have developed an algorithm called MC-MinH that uses the min-wise hashing approach, along with a greedy clustering algorithm to group 16S and whole metagenomic sequences. We represent unequal length sequences using contiguous subsequences or k-mers, and then approximate the computation of pairwise similarity using independent min-wise hashing. The performance of our algorithm is evaluated on several real and simulated metagenomic benchmarks. We demonstrate that our approach is computationally efficient and produces accurate clustering results when evaluated using external ground truth.